With large-scale well-labeled datasets, deep learning has shown significant success in medical image segmentation. However, it is challenging to acquire abundant annotations in clinical practice due to extensive expertise requirements and costly labeling efforts. Recently, contrastive learning has shown a strong capacity for visual representation learning on unlabeled data, achieving impressive performance rivaling supervised learning in many domains. In this work, we propose a novel multi-scale multi-view global-local contrastive learning (MMGL) framework to thoroughly explore global and local features from different scales and views for robust contrastive learning performance, thereby improving segmentation performance with limited annotations. Extensive experiments on the MM-WHS dataset demonstrate the effectiveness of MMGL framework on semi-supervised cardiac image segmentation, outperforming the state-of-the-art contrastive learning methods by a large margin.
翻译:由于大量的专门知识要求和代价高昂的标签工作,在医疗图象分割方面,深层次的学习显示在医学图象分割方面取得巨大成功,然而,由于广泛的专门知识要求和昂贵的标签工作,很难在临床实践中获得大量说明。最近,对比鲜明的学习显示,在未贴标签的数据方面,有很强的视觉代表性学习能力,在许多领域取得了令人印象深刻的与监督的学习相匹配的业绩。在这项工作中,我们提议了一个新的多层次的多视角全球-地方对比学习(MMMGL)框架,以便从不同的尺度和观点全面探索全球和地方特点,实现强健的对比学习业绩,从而以有限的注释改进分化性。 MM-HMS数据集的广泛实验表明MGL框架在半监督的心像分割方面的效力,大大优于最先进的对比性学习方法。